Mean Field Theory for Sigmoid Belief Networks

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6652

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DC Field	Value	Language
dc.creator	Saul, Lawrence K.	-
dc.creator	Jaakkola, Tommi	-
dc.creator	Jordan, Michael I.	-
dc.date	2004-10-08T20:36:26Z	-
dc.date	2004-10-08T20:36:26Z	-
dc.date	1996-08-01	-
dc.date.accessioned	2013-10-09T02:46:22Z	-
dc.date.available	2013-10-09T02:46:22Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1570	-
dc.identifier	http://hdl.handle.net/1721.1/6652	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. Our mean field theory provides a tractable approximation to the true probability distribution in these networks; it also yields a lower bound on the likelihood of evidence. We demonstrate the utility of this framework on a benchmark problem in statistical pattern recognition -- the classification of handwritten digits.	-
dc.format	269766 bytes	-
dc.format	412589 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1570	-
dc.title	Mean Field Theory for Sigmoid Belief Networks	-
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